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<Paper uid="C94-1012">
  <Title>Coping With Ambiguity in a Large-Scale Machine Translation System</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In an interlingual knowledge-based machine translation system, ambiguity arises when the source 1.qnguage analyzer produces more than one interlingua expression for a source sentence. This can have a negative impact on translation quality, since a target sentence may be produced from an unintended meaning. In this paper we describe the ,nethods nsed in the KANT machine translation system to reduce or eliminate ambiguity in a large-scale application domain. We also test these methods on a large corpus of test sentences, in order to illustrate how the different disambiguation methods redtuce the average number of parses per sentence,</Paragraph>
  </Section>
class="xml-element"></Paper>